Trasmisión adaptativa de video sobre redes definidas por software

This paper presents the results of a study on the evaluation of adaptive transmission of video streams using the DASH technique on Software Defined Networks. There are also presented in this document, the description of the tools required for the implementation of the evaluation, as well as a description of the methodology used for the development of the experiments. In addition, the results of an adaptive transmission of a video by using DASH are presented. This transmission was carried out over a software defined network emulated on MININET. The results show that DASH technique easily allows to implement video streaming services that can adapt the quality of the transmission according to the resources available in the network.En este artículo se presentan los resultados de un estudio relacionado con la evaluación de la transmisión adaptativa de flujos de video usando el estándar DASH sobre escenarios de redes definidas por software. Dentro de los aspectos que se presentan en este documento está la descripción de las herramientas software necesarias para la implementación de la evaluación, así como la metodología de uso de estas. Además, se presentan los resultados de un experimento de emulación de una topología de red definida por software en la plataforma MININET y la transmisión adaptativa de un video mediante DASH. Los resultados muestran que la técnica DASH permite fácilmente la implementación de servicios de video streaming que son capaces de adaptarse a los recursos disponibles en la red. También se resalta la facilidad de experimentar con las redes definidas por software en la plataforma de emulación utilizada y la configuración de servicios multimedia sobre este tipo de redes

A quality of experience approach in smartphone video selection framework for energy efficiency

Online video streaming is getting more common in the smartphone device nowadays. Since the Corona Virus (COVID-19) pandemic hit all human across the globe in 2020, the usage of online streaming among smartphone user are getting more vital. Nevertheless, video streaming can cause the smartphone energy to drain quickly without user to realize it. Also, saving energy alone is not the most significant issues especially if with the lack of attention on the user Quality of Experience (QoE). A smartphones energy management is crucial to overcome both of these issues. Thus, a QoE Mobile Video Selection (QMVS) framework is proposed. The QMVS framework will govern the tradeoff between energy efficiency and user QoE in the smartphone device. In QMVS, video streaming will be using Dynamic Video Attribute Pre-Scheduling (DVAP) algorithm to determine the energy efficiency in smartphone devices. This process manages the video attribute such as brightness, resolution, and frame rate by turning to Video Content Selection (VCS). DVAP is handling a set of rule in the Rule Post-Pruning (RPP) method to remove an unused node in list tree of VCS. Next, QoE subjective method is used to obtain the Mean Opinion Score (MOS) of users from a survey experiment on QoE. After both experiment results (MOS and energy) are established, the linear regression technique is used to find the relationship between energy consumption and user QoE (MOS). The last process is to analyze the relationship of VCS results by comparing the DVAP to other recent video streaming applications available. Summary of experimental results demonstrate the significant reduction of 10% to 20% energy consumption along with considerable acceptance of user QoE. The VCS outcomes are essential to help users and developer deciding which suitable video streaming format that can satisfy energy consumption and user QoE